System and method for fine-tuning an existing machine learning model using out-of-domain data

Abstract

Systems, methods, and computer-readable media are provided for accessing out-of-domain training data that includes items of non-textual digital media content. Each of the items is labeled with text and background characteristic(s) that indicate an origination category of candidate origination categories for the item. A pre-trained model is used to generate vector embeddings of the out-of-domain training data and a particular vector embedding of a particular item of in-domain data that is labeled with text but is not labeled with any background characteristic(s) that indicate any origination categories. The generated vector embeddings are used to train another machine learning model to predict the background characteristic(s) based on vector embeddings of non-textual digital media content. The other machine learning model is further used to determine out-of-domain vector embeddings corresponding to the vector embeddings of the out-of-domain training data and in-domain vector embedding(s) corresponding to the in-domain data. Distances are determined between out-of-domain and in-domain vector embedding(s). Based on the distances, a textual content generation model is tuned on item(s) of the out-of-domain data. The item(s) of out-of-domain data to use for tuning may be selected and/or ordered based on the distances. A resulting model may be stored and used to transform unlabeled item(s) of non-textual content to textual content.